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Deep Learning-Based Gear Pitting Severity Assessment using Acoustic Emission, Vibration and Currents signals

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dc.contributor.authorMedina, Ruben
dc.contributor.authorCerrada, Mariela
dc.contributor.authorCabrera, Diego
dc.contributor.authorSanchez, Rene-Vinicio
dc.contributor.authorLi, Chuan
dc.contributor.authorValente de Oliveira, José
dc.date.accessioned2020-07-24T10:53:10Z
dc.date.available2020-07-24T10:53:10Z
dc.date.issued2019
dc.description.abstractA method for gearbox pitting faults severity classification using Deep Learning techniques is reported. The signals are preprocessed for obtaining a 2D time-frequency representation corresponding to the Mel Frequency Cepstral Coefficients. This bi-dimensional representation is the feature space used for classification. A Long Short Term Memory network (LSTM) is used for classifying nine levels of pitting in spur gears. Each signals dataset is used for training and validating a LSTM network. Classification accuracies up to 100 % are obtained during cross-validation with the analyzed signals dataset.
dc.description.sponsorshipMOST Science and Technology Partnership Program [KY201802006]
dc.description.sponsorshipUniversidad Politecnica Salesiana through the research group GIDTEC
dc.identifier.doi10.1109/PHM-Paris.2019.00042
dc.identifier.isbn978-1-7281-0329-7
dc.identifier.issn2166-5656
dc.identifier.urihttp://hdl.handle.net/10400.1/14460
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relation.ispartofseriesPrognostics and System Health Management Conference
dc.subjectFault-Diagnosis
dc.subjectFrequency
dc.titleDeep Learning-Based Gear Pitting Severity Assessment using Acoustic Emission, Vibration and Currents signals
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage216
oaire.citation.startPage210
oaire.citation.title2019 Prognostics and System Health Management Conference (PHM-Paris)
oaire.citation.titleParis, France
person.familyNameLUÍS VALENTE DE OLIVEIRA
person.givenNameJOSÉ
person.identifier.ciencia-id1F12-C1D3-7717
person.identifier.orcid0000-0001-5337-5699
rcaap.rightsrestrictedAccess
rcaap.typeconferenceObject
relation.isAuthorOfPublicationbb726e73-690c-4a33-822e-c47bdac3035b
relation.isAuthorOfPublication.latestForDiscoverybb726e73-690c-4a33-822e-c47bdac3035b

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